Quadrature spatial modulation–performance analysis and impact of imperfect channel knowledge
Bibliographic record
Abstract
Abstract In this paper, a new multiple‐input multiple‐output transmission technique calledquadrature spatial modulation (QSM)is proposed and analyzed in the presence of imperfect channel estimation at the receiver. In QSM, conventional spatial constellation diagram of spatial modulation (SM) system is expanded to include both in‐phase and quadrature components. As such, significant enhancement in the overall spectral efficiency is achieved while retaining all inherent advantages of SM technique, such as inter‐channel interference avoidance, single radio frequency chain transmitter and low receiver complexity. It is shown that significant performance enhancements can be achieved as compared with SM, Alamouti, and spatial multiplexing systems. Besides, the impact of Gaussian imperfect channel estimation on the performance of QSM system is studied. A closed‐form expression for the pairwise error probability of generic QSM system is derived and used to calculate a tight upper bound of the average bit error probability over Rayleigh fading channels with perfect and imperfect channel knowledge. Also, simple asymptotic expression is derived and analyzed. Obtained Monte Carlo simulation results highlight the accuracy of the conducted analysis. Copyright © 2014 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".